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Related Concept Videos

Theoretical Approaches to Psychological Disorder01:29

Theoretical Approaches to Psychological Disorder

742
The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
Biological approach
The biological approach posits that internal, organic factors are the primary causes of such disorders. This perspective emphasizes brain structure and function, genetic predispositions, and neurotransmitter imbalances. For example, schizophrenia has been associated with both genetic...
742
Stress and Mental Health01:30

Stress and Mental Health

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Chronic stress profoundly affects mental health, significantly influencing mood, behavior, and overall quality of life. Research closely links chronic stress with mental health conditions such as depression, anxiety, and substance use disorders. Ongoing exposure to stress can lead to physiological and psychological changes, initiating a cycle of emotional distress and maladaptive coping mechanisms.
Individuals with depression often experience challenges in both their personal and professional...
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Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

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Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
464
Community Based Intervention01:30

Community Based Intervention

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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
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VQProtect: Lightweight Visual Quality Protection for Error-Prone Selectively Encrypted Video Streaming.

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Updated: Jan 13, 2026

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A Systematic Review of Federated and Cloud Computing Approaches for Predicting Mental Health Risks.

Iram Fiaz1, Nadia Kanwal1, Amro Al-Said Ahmad1

  • 1School of Computer Science and Mathematics, Keele University, Newcastle ST5 5BG, UK.

Sensors (Basel, Switzerland)
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Federated learning (FL) enables privacy-preserving analysis of sensitive digital mental health data across devices. While promising, progress is fragmented, requiring more work on real-world deployment and ethical standards.

Keywords:
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Area of Science:

  • Digital health
  • Mental health technology
  • Machine learning

Background:

  • Mental health disorders are a leading cause of disability globally.
  • Digital health technologies generate valuable behavioral data for personalized care.
  • Data privacy and scalability are major challenges in analyzing sensitive mental health data.

Purpose of the Study:

  • To review the progress and challenges of using federated learning (FL) for digital mental health.
  • To identify key areas for developing robust FL systems in mental healthcare.

Main Methods:

  • Systematic review of 1104 records, with 17 empirical studies selected based on quality.
  • Analysis of selected studies focused on FL and edge/cloud architectures, data sources, privacy techniques, and real-world application.
  • Comparison of studies based on their methodologies and findings.

Main Results:

  • Federated learning combined with edge/fog/cloud computing shows potential for real-time mental health analysis.
  • Progress in FL for digital mental health is innovative but fragmented.
  • Limited research exists on comorbidity modeling, deployment evaluation, and standardized benchmarks.

Conclusions:

  • Federated learning offers a privacy-preserving approach for analyzing digital mental health data.
  • Further development is needed in areas like deployment, evaluation, and standardization for scalable and ethical FL systems.
  • Priorities include addressing comorbidity, real-world validation, and establishing common benchmarks.